Devising Isolation Forest-Based Method to Investigate the sRNAome of Mycobacterium tuberculosis Using sRNA-seq Data.
Bioinform Biol Insights
; 18: 11779322241263674, 2024.
Article
de En
| MEDLINE
| ID: mdl-39091283
ABSTRACT
Small non-coding RNAs (sRNAs) regulate the synthesis of virulence factors and other pathogenic traits, which enables the bacteria to survive and proliferate after host infection. While high-throughput sequencing data have proved useful in identifying sRNAs from the intergenic regions (IGRs) of the genome, it remains a challenge to present a complete genome-wide map of the expression of the sRNAs. Moreover, existing methodologies necessitate multiple dependencies for executing their algorithm and also lack a targeted approach for the de novo sRNA identification. We developed an Isolation Forest algorithm-based method and the tool Prediction Of sRNAs using Isolation Forest for the de novo identification of sRNAs from available bacterial sRNA-seq data (http//posif.ibab.ac.in/). Using this framework, we predicted 1120 sRNAs and 46 small proteins in Mycobacterium tuberculosis. Besides, we highlight the context-dependent expression of novel sRNAs, their probable synthesis, and their potential relevance in stress response mechanisms manifested by M. tuberculosis.
Texte intégral:
1
Collection:
01-internacional
Base de données:
MEDLINE
Langue:
En
Journal:
Bioinform Biol Insights
Année:
2024
Type de document:
Article
Pays d'affiliation:
Inde
Pays de publication:
États-Unis d'Amérique